System, method and computer program product for temperature-aware task scheduling

ABSTRACT

A temperature-aware task scheduling method, system, and computer program product, include determining a change in an operation intensity factor of the GPU from a previous state and modifying the operation intensity factor, in response to the determining the change in the operation intensity factor from the previous state.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S. patentapplication Ser. No. 15/238,258, filed on Aug. 16, 2016, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to a temperature-aware taskscheduling method for one or more graphical processing units (GPUs), andmore particularly, but not by way of limitation, to a system, method,and recording medium for scheduling a task for a GPU based on atemperature and an intensiveness of each of an arithmetic logic unit(ALU) and a dynamic random-access memory (DRAM) component.

Rapid evolution of GPUs in performance, architecture, andprogrammability provides general and scientific computational potentialfar beyond their primary purpose of Graphical processing. GraphicalProcessing Units (GPUs) are pervasive in cognitive applications, forexample. GPUs are readily available for cloud computing. For GPU users(e.g., single workload), performance is key and on which most of theconventional techniques focus improvements. For GPU owners, resourceutilization is key based on how to compact more workloads into limitedresources.

Conventional techniques have only considered monitoring a totaltemperature of the GPU when for example such total temperature is drivenby an arithmetic logic unit (ALU) component, which is heavily loaded andoverheated, while at the same time a DRAM unit component is not heavilyloaded. Alternatively, the total temperature can be driven by a DRAMunit that is heavily loaded, and may become overheated, while an ALUunit is not loaded heavily.

There is a need in the art to consider a temperature difference of ALUand DRAM inside a GPU unit such that the ALU and DRAM can be overheatedwith a new task.

SUMMARY

In an exemplary embodiment, the present invention provides acomputer-implemented method for scheduling a task on a graphicalprocessing unit (GPU), the method including, the GPU, receiving arequest to execute a task collecting task information (including anintensiveness factor) of a computation by an internal arithmetic logicunit (ALU) and a dynamic random-access memory (DRAM) for a task,obtaining a temperature of the ALU and the DRAM, and accepting the taskto the GPU based on the intensiveness factor, ALU temperature and DRAMtemperature.

One or more other exemplary embodiments include a computer programproduct and a system.

Other details and embodiments of the invention will be described below,so that the present contribution to the art can be better appreciated.Nonetheless, the invention is not limited in its application to suchdetails, phraseology, terminology, illustrations and/or arrangements setforth in the description or shown in the drawings. Rather, the inventionis capable of embodiments in addition to those described and of beingpracticed and carried out in various ways and such description shouldnot be regarded as limiting.

Thus, those skilled in the art will appreciate that the invention uponwhich this disclosure is based may readily be utilized as a basis forthe designing of equivalent structures, methods and systems that maycarry out one or more purposes of the present invention. It isimportant, therefore, that the claims be regarded as including suchequivalents, insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention will be better understood from the followingdetailed description of the exemplary embodiments of the invention withreference to the drawings, in which:

FIG. 1 shows a high-level flow chart for a temperature-aware taskscheduling method according to an embodiment of the present invention.

FIG. 2 exemplarily shows a component configuration of a GPU compared toa CPU according to an embodiment of the present invention.

FIG. 3 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIG. 1-5, in whichlike reference numerals refer to like parts throughout. It is emphasizedthat, according to common practice, the various features of the drawingare not necessarily to scale. On the contrary, the dimensions of thevarious features can be arbitrarily expanded or reduced for clarity.

By way of overview with reference to the example depicted in FIG. 1, thetemperature-aware task scheduling method 100 in accordance with thepresent invention includes scheduling a task based on temperaturemeasurements of the ALU and DRAM, as well as an intensiveness factorassociated with the task and determining whether the ALU and DRAM mayexceed a threshold intensiveness factor. With reference now to FIG. 3,one or more computers of a computer system 12 according to an embodimentof the present invention can include a memory 28 having instructionsstored in a storage system to perform the steps of FIG. 1.

Although one or more embodiments (see e.g., FIGS. 3-5) may beimplemented in a cloud environment 50 (see e.g., FIG. 4), it isnonetheless understood that the present invention can be implementedoutside of the cloud environment.

Referring again to FIG. 1, in step 101, a new task for the GPU toprocess is received.

In step 102, an intensiveness factor for each of the computationalrequirement of the new task (I_alu) and the memory usage requirement ofthe new task (I_dram) is determined if not already known. For example,if the new task had been previously run on the GPU, the intensivenesswould already be known.

An intensiveness factor of the computational requirements can refer to aprocessing requirement associated with the task and therefor, apotential resulting temperature increase of the ALU performing the task.In general, the greater the processing requirement for a computation,the higher the corresponding intensiveness factor will be. Similarly, anintensiveness factor associated with a memory usage requirement canrefer to the processing requirements of the DRAM and the resultingtemperature increase that will occur. In general, the greater theprocessing requirement for memory usage, the higher the intensivenessfactor will be for the memory usage requirement. That is, theintensiveness factor reflects the temperature increase for each of theALU and the DRAM, independently of each other, that will occur when thenew task is processed by the GPU.

In some embodiments, in steps 101 and 102, an intensiveness factor(s)for a computation by an ALU and for a memory usage by DRAM) is collectedfor a new task to be performed by a GPU.

In some embodiments, the intensiveness factor of the new task can be auser input value. Alternatively, in Step 107, if the intensivenessfactor is unknown (e.g., “NO”), the intensiveness factor can be learnedfrom a task signature of the new task. That is, each new task isassociated with a task signature. The task signature is based on thetask parameters, and their access mode (e.g., read or write), and thisinformation can be used to learn the intensiveness factor.

In step 103, if the intensiveness factor is known (e.g., “YES”), atemperature of each of the ALU (t_alu) and DRAM (t_dram) is obtainedindependently of each other. In step 103, a temperature is obtained onlyfor the ALU and a temperature is obtained only for the DRAM.

With reference now to the example depicted in FIG. 2, ALU and DRAM unitsof a GPU are spaced apart by a gap (e.g., physically separated on theGPU) such that a temperature of the ALU can be very different from atemperature of the DRAM. Thermal conductance does not make theirtemperature the same.

In such example, in step 103, the independent temperatures can beobtained via two sensors (e.g., thermometers) that monitor the ALUtemperature and the DRAM temperature in real time, independently of eachother.

In step 104, the new task is accepted if after the new task is run, theintensiveness factor of each of the ALU and the DRAM will not cause thetemperature of the ALU or the DRAM to exceed a predetermined threshold.For example, let's assume that a temperature of the ALU is 50 degreesFahrenheit and a temperature of the DRAM is 25 degrees Fahrenheit andthe ALU has a predetermined threshold temperature value of 80 degreesFahrenheit and the DRAM has a predetermined threshold temperature of 45degrees Fahrenheit. In this example, if the intensiveness factor causeseither of the DRAM or the ALU to have a temperature which exceeds thepredetermined threshold temperature (e.g, 45 degrees Fahrenheit for theDRAM and 80 degrees Fahrenheit for the ALU), the new task will not beaccepted.

In other words, in step 104, the intensiveness factor and thetemperature for each of the ALU and DRAM can be weighed (e.g., adecision made based on) to determine if the new task will cause eitherof the temperatures to exceed a predetermined threshold and based on thedecision, accept or reject the new task. Thus, new tasks can beprocessed more efficiently by maintaining a temperature that does notreduce efficiency of the GPU.

Again, in step 104, a new task can be scheduled for execution on the GPUif the intensiveness factor causes each of the ALU temperature and theDRAM temperature to remain below a predetermined threshold value.

In step 105, if the new task is accepted for execution by the GPU, theALU and the DRAM of the GPU is observed to adjust the intensivenessfactor for the new task for each of the ALU and the DRAM. In step 105,the new task is observed as it is being run by the GPU, including anychange in temperature of the ALU and DRAM (e.g., Δt_alu and Δt_dram) andthe intensiveness factor for the ALU and the DRAM is adjusted, based onthe observed change in temperature.

In some embodiments, in step 105, the new task being run is observed tocalculate a change in the ALU temperature and in the DRAM temperature toadjust the intensiveness factor for each of the ALU and the DRAM.

In step 106, a condition to accept the new task based on a priorexecution of the new task can be learned. That is, in step 106, theacceptance conditions of the new task can be updated over time. Forexample, it is known that as a GPU ages, its performance can decrease.In another example, a GPU's performance and thermal characteristics maychange based on its adjustment to a different working frequency. s.Therefore, in some embodiments, the GPU's acceptance of new tasks maycorrespondingly adjust over time as the properties of the GPU changes.

Alternatively, the condition for accepting the new task in step 106 canbe based on a user input.

It is noted that although the embodiments described herein referred to aGPU, the method 100 can be applied to any processor having multipleprocessing components that a temperature of each processing componentcan be individually measured to efficiently schedule tasks. For example,the method 100 can be utilized with the CPU shown in FIG. 2 because theALU and the DRAM are separated and the temperature of each can beindependently determined.

Further, it is noted that although embodiments are described herein withregard to a single GPU, the method 100 can be used on a plurality ofGPUs and scheduling the tasks accordingly.

That is, in a multi-GPU (or processing environment), the invention alsoallows one to decide which GPU (processor) to assign a new task from aqueue depending upon its intensiveness and its effect on temperatures ofthe ALU and DRAM (other processing components).

Exemplary Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 3, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablenode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth herein.

Although cloud computing node 10 is depicted as a computer system/server12, it is understood to be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with computersystem/server 12 include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop circuits, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems orcircuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

Referring again to FIG. 3, computer system/server 12 is shown in theform of a general-purpose computing circuit. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 4 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 5, an exemplary set of functional abstractionlayers provided by cloud computing environment 50 (FIG. 4) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 5 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the temperature-aware task scheduling method 100.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computer-implemented method for a task on agraphical processing unit (GPU), the method comprising: determining achange in an operation intensity factor of the GPU from a previousstate; and modifying the operation intensity factor, in response to saiddetermining the change in the operation intensity factor from theprevious state.
 2. The method of claim 1, wherein the operationintensity factor includes a temperature of an arithmetic logic unit(ALU) and a temperature of a dynamic random-access memory (DRAM) duringa computation by the ALU and a memory usage of the DRAM for the task. 3.The method of claim 1, further comprising accepting and executing a taskto the GPU based on the operation intensity factor.
 4. The method ofclaim 2, wherein the temperature of the ALU and the temperature of theDRAM obtained independently from each other.
 5. The method of claim 2,further comprising accepting and executing a task to the GPU based onthe operation intensity factor, the ALU temperature, and the DRAMtemperature.
 6. The method of claim 4, further comprising accepting andexecuting a task to the GPU based on the operation intensity factor, theALU temperature, and the DRAM temperature.
 7. The method of claim 3,wherein the accepting accepts the task if the operation intensity factorresults in each of an arithmetic logic unit (ALU) and a dynamicrandom-access memory (DRAM) temperature to remain below a predeterminedthreshold value.
 8. The computer-implemented method of claim 1, embodiedin a cloud-computing environment.
 9. A computer program product fortemperature-aware task scheduling a task on a graphical processing unit(GPU), the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a computer to cause the computer toperform: determining a change in an operation intensity factor of theGPU from a previous state; and modifying the operation intensity factor,in response to said determining the change in the operation intensityfactor from the previous state.
 10. A temperature-aware task schedulingsystem for a task on a graphical processing unit (GPU), said systemcomprising: a processor; and a memory, the memory storing instructionsto cause the processor to: determining a change in an operationintensity factor of the GPU from a previous state; and modifying theoperation intensity factor, in response to said determining the changein the operation intensity factor from the previous state.
 11. Thesystem of claim 10, embodied in a cloud-computing environment.